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Registro Completo |
Biblioteca(s): |
Embrapa Instrumentação. |
Data corrente: |
16/11/2021 |
Data da última atualização: |
09/06/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
FURUYA, D. E. G.; MA, L.; PINHEIRO, M. M. F.; GOMES, F. D. G.; GONÇALVEZ, W. N.; MARCATO JUNIOR, J.; RODRIGUES, D. de C.; BLASSIOLI- MORAES, M. C.; MICHEREFF, M. F. F.; BORGES, M.; ALAUMANN, R. A.; FERREIRA, E. J.; OSCO, L. P.; RAMOS, A. P. M.; LI, J.; JORGE, L. A. de C. |
Afiliação: |
MARIA CAROLINA BLASSIOLI MORAES, Cenargen; MIGUEL BORGES, Cenargen; EDNALDO JOSE FERREIRA, CNPDIA; LUCIO ANDRE DE CASTRO JORGE, CNPDIA. |
Título: |
Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
International Journal of Applied Earth Observation and Geoinformation, v. 105, 102608, 2021. |
Páginas: |
1 - 10 |
ISSN: |
0303-2434 |
DOI: |
https://doi.org/10.1016/j.jag.2021.102608 |
Idioma: |
Inglês |
Conteúdo: |
Accurately detecting the insect damage caused in plants might reduce losses in crop yields. Hyperspectral data is a well-accepted data source to attend this issue. However, due to their high dimensional, both robust and intelligent methods are required to extract information from these datasets. Therefore, we explore the processing of hyperspectral data with artificial intelligence methods joined with clustering techniques to detect insect herbivory damage in maize plants. We measured the leaf spectral response from three different groups of maize plants: control (undamaged plants); damaged by Spodoptera frugiperda herbivory, and damaged by Dichelops meiacanthus. Data were collected with a FieldSpec 3.0 Spectroradiometer from 350 to 2500 nm for eight consecutive days. We adjusted eight machine learning methods. We also determined the most contributive wavelengths to differentiate undamaged from damaged plants by insect herbivore attack using clustering strategy. For that, we applied the clusterization method based on a self-organizing map (SOM). The Random Forest (RF) model is the overall best learner, and up to the 5th day of analysis represents the most adequate day to segregate maize undamaged from damaged maize. RF was able to separate the three groups of treatments with an F1-measure of up to 96.7% (Recall of 96.7% and Precision of 96.7%). Additionally, we found out that the most representative spectral regions are located in the near-infrared range. Our approach consists of an original contribution to early differentiate the undamaged plant from the damaged one due to insect-attack, highlighting the most contributive wavelengths to map this occurrence. MenosAccurately detecting the insect damage caused in plants might reduce losses in crop yields. Hyperspectral data is a well-accepted data source to attend this issue. However, due to their high dimensional, both robust and intelligent methods are required to extract information from these datasets. Therefore, we explore the processing of hyperspectral data with artificial intelligence methods joined with clustering techniques to detect insect herbivory damage in maize plants. We measured the leaf spectral response from three different groups of maize plants: control (undamaged plants); damaged by Spodoptera frugiperda herbivory, and damaged by Dichelops meiacanthus. Data were collected with a FieldSpec 3.0 Spectroradiometer from 350 to 2500 nm for eight consecutive days. We adjusted eight machine learning methods. We also determined the most contributive wavelengths to differentiate undamaged from damaged plants by insect herbivore attack using clustering strategy. For that, we applied the clusterization method based on a self-organizing map (SOM). The Random Forest (RF) model is the overall best learner, and up to the 5th day of analysis represents the most adequate day to segregate maize undamaged from damaged maize. RF was able to separate the three groups of treatments with an F1-measure of up to 96.7% (Recall of 96.7% and Precision of 96.7%). Additionally, we found out that the most representative spectral regions are located in the near-infrared range. Our approach consis... Mostrar Tudo |
Palavras-Chave: |
Proximal hyperspectral sensing; Random forest. |
Categoria do assunto: |
-- |
Marc: |
LEADER 02800naa a2200361 a 4500 001 2136152 005 2022-06-09 008 2021 bl uuuu u00u1 u #d 022 $a0303-2434 024 7 $ahttps://doi.org/10.1016/j.jag.2021.102608$2DOI 100 1 $aFURUYA, D. E. G. 245 $aPrediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data.$h[electronic resource] 260 $c2021 300 $a1 - 10 520 $aAccurately detecting the insect damage caused in plants might reduce losses in crop yields. Hyperspectral data is a well-accepted data source to attend this issue. However, due to their high dimensional, both robust and intelligent methods are required to extract information from these datasets. Therefore, we explore the processing of hyperspectral data with artificial intelligence methods joined with clustering techniques to detect insect herbivory damage in maize plants. We measured the leaf spectral response from three different groups of maize plants: control (undamaged plants); damaged by Spodoptera frugiperda herbivory, and damaged by Dichelops meiacanthus. Data were collected with a FieldSpec 3.0 Spectroradiometer from 350 to 2500 nm for eight consecutive days. We adjusted eight machine learning methods. We also determined the most contributive wavelengths to differentiate undamaged from damaged plants by insect herbivore attack using clustering strategy. For that, we applied the clusterization method based on a self-organizing map (SOM). The Random Forest (RF) model is the overall best learner, and up to the 5th day of analysis represents the most adequate day to segregate maize undamaged from damaged maize. RF was able to separate the three groups of treatments with an F1-measure of up to 96.7% (Recall of 96.7% and Precision of 96.7%). Additionally, we found out that the most representative spectral regions are located in the near-infrared range. Our approach consists of an original contribution to early differentiate the undamaged plant from the damaged one due to insect-attack, highlighting the most contributive wavelengths to map this occurrence. 653 $aProximal hyperspectral sensing 653 $aRandom forest 700 1 $aMA, L. 700 1 $aPINHEIRO, M. M. F. 700 1 $aGOMES, F. D. G. 700 1 $aGONÇALVEZ, W. N. 700 1 $aMARCATO JUNIOR, J. 700 1 $aRODRIGUES, D. de C. 700 1 $aBLASSIOLI- MORAES, M. C. 700 1 $aMICHEREFF, M. F. F. 700 1 $aBORGES, M. 700 1 $aALAUMANN, R. A. 700 1 $aFERREIRA, E. J. 700 1 $aOSCO, L. P. 700 1 $aRAMOS, A. P. M. 700 1 $aLI, J. 700 1 $aJORGE, L. A. de C. 773 $tInternational Journal of Applied Earth Observation and Geoinformation$gv. 105, 102608, 2021.
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Embrapa Instrumentação (CNPDIA) |
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Registro Completo
Biblioteca(s): |
Embrapa Agroindústria Tropical. |
Data corrente: |
15/02/2008 |
Data da última atualização: |
05/02/2009 |
Tipo da produção científica: |
Artigo em Anais de Congresso / Nota Técnica |
Circulação/Nível: |
-- - -- |
Autoria: |
ARAÚJO, M. R. A. de; VASCONCELOS, H. E. M. |
Afiliação: |
Marcelo Renato Alves de Araújo, CNPC; Helenira Ellery M. Vasconcelos, CNPAT. |
Título: |
Melhoramento genético participativo: uma estratégia para os ambientes adversos do semi-árido nordestino. |
Ano de publicação: |
2007 |
Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE SISTEMAS DE PRODUÇÃO, 7., 2007, Fortaleza-CE, 2007. Agricultura familiar, políticas públicas e inclusao social: anais. Fortaleza: Embrapa Recursos Genéticos e Biotecnologia, 2007. |
Páginas: |
10 p. |
Idioma: |
Português |
Categoria do assunto: |
-- |
Marc: |
LEADER 00593naa a2200133 a 4500 001 1427028 005 2009-02-05 008 2007 bl uuuu u00u1 u #d 100 1 $aARAÚJO, M. R. A. de 245 $aMelhoramento genético participativo$buma estratégia para os ambientes adversos do semi-árido nordestino. 260 $c2007 300 $a10 p. 700 1 $aVASCONCELOS, H. E. M. 773 $tIn: CONGRESSO BRASILEIRO DE SISTEMAS DE PRODUÇÃO, 7., 2007, Fortaleza-CE, 2007. Agricultura familiar, políticas públicas e inclusao social: anais. Fortaleza: Embrapa Recursos Genéticos e Biotecnologia, 2007.
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